I think you are going down the right path for detecting motions like a "frisbee-throw."

Like you said, I would sample data for the "ideal" frisbee throw motion. Then use that data to create a array of vectors which define the general shape or spline of the throw motion. Then transform that shape into a common reference frame so that the acceleration values are similar no matter which direction you move your phone during a throw. Now the values will differ a good amount for each throw, but the general shape should be similar. Just find an appropriately large epsilon to compare the throw shapes.

There are a number of ways you can tackle this problem, and this is just the first idea that came to my head.

Another idea would be to utilize the iPhone 4's gyroscope for more accurate data. But, that would limit your audience because it is only available with the iPhone 4. I'd also recommend watching the 'Device Motion' WWDC session if you're a registered apple developer.

Thanks, i am trying to get pattern data which can be easily calculate device motion.
I try to draw graphs of value x, y, z. Most of the time graph pattern is same.
Here my problem is how we compare user acceleration data with sample pattern data.